TY - GEN
T1 - Real-Time Age Detection Using a Convolutional Neural Network
AU - Sithungu, Siphesihle
AU - Van der Haar, Dustin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - The problem of determining people’s age is a recurring theme in areas such as law enforcement, education and sports because age is often used to determine eligibility. The aim of current work is to make use of a lightweight machine learning model for automating the task of detecting people’s age. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females, in real-time. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. To evaluate the model’s performance in real-time, Haar Cascades were used to detect faces from video feeds. The detected faces were fed to the model for it to make age predictions. Experimental results showed that age-detection can be performed in real-time. Although, the prediction accuracy of the model requires improvement.
AB - The problem of determining people’s age is a recurring theme in areas such as law enforcement, education and sports because age is often used to determine eligibility. The aim of current work is to make use of a lightweight machine learning model for automating the task of detecting people’s age. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females, in real-time. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. To evaluate the model’s performance in real-time, Haar Cascades were used to detect faces from video feeds. The detected faces were fed to the model for it to make age predictions. Experimental results showed that age-detection can be performed in real-time. Although, the prediction accuracy of the model requires improvement.
KW - Age detection
KW - Computer vision
KW - Convolutional Neural Network
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85068113200&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20482-2_20
DO - 10.1007/978-3-030-20482-2_20
M3 - Conference contribution
AN - SCOPUS:85068113200
SN - 9783030204815
T3 - Lecture Notes in Business Information Processing
SP - 245
EP - 256
BT - Business Information Systems - 22nd International Conference, BIS 2019, Proceedings
A2 - Abramowicz, Witold
A2 - Corchuelo, Rafael
PB - Springer Verlag
T2 - 22nd International Conference on Business Information Systems, BIS 2019
Y2 - 26 June 2019 through 28 June 2019
ER -